BRIDGING RESEARCH AND PRACTICE ON PERSONAL PRODUCTIVITY | By Jill Duffy

‘The Data Don’t Apply to Me!’ or Hoping for Personalized Data in an Age of Science

“If it were not for the great variability among individuals,
medicine might as well be a science, not an art.”
–Sir William Osler, 1892 (as cited in Issa, 2007)

“I am a singular person with unique habits, actions, thoughts, and reactions. These research findings don’t apply to me.”

I hear people reject research findings all the time, saying the data don’t apply to them. “I don’t need as much sleep as this study says,” they argue. Or, “Sure, maybe some people are more productive in the morning, but not me. I’m a night owl.” Or, “This research on obese people and lower productivity is written by a bunch of fat-ist who are stereotyping fat people as lazy. I’m overweight, and I’m very productive!”

Sometimes it’s for good reason. There is a lot of bad research out there. Additionally, there are too many popular articles that claim to summarize research but only highlight one or two findings out of context, usually making them out to be more than they are. A catchy stat will always get more readers than a long and nuanced assessment of data.

Other times, it’s not the article’s fault but the reader’s. Sometimes, people only read a headline and skip the article, assuming everything they need to know is in that headline. Headlines alone never contain the full context necessarily. That’s why there are stories to go with them.

And in the days of online reading, headlines are often intentionally misleading as a way to get readers to click on them.

But there is a larger issue, too, that of people wanting to believe that they are special goddamned snowflakes.

We all do it. Most of us believe we are rational human beings and that all our choices and actions are based on logical thinking and circumstances that are particular to us, rather than us being in a numbers game where we are whatever the average is. We think we are outliers. We think we are special.

The problem with taking that idea and using it to reject research findings, however, is that it illustrates a faulty understand of what research is.

Warning:Fair warning that in this next section, I am going to generalize a lot to make a point. In fact, generalizing is kind of the whole point. So just bear with me if you don’t agree with every little detail.

Research vs Personalized Data

In fields that deal with human beings, research findings generally describe what happens to most people under certain conditions.

Definitionally, they do apply to you.

Or more accurately, it is likely that they apply to you.

Even more accurately: It is likely that they apply to you if you also fit the description of the subject pool, and whatever other factors are taken into consideration.

Let’s say a medication is shown to cure a condition in 99.8 percent of otherwise healthy adults who have it, and you are an otherwise healthy adult who has it. There is a 0.2 percent chance that the medication won’t work for you, i.e., that you are a special goddamned snowflake. Medications usually only get to market if the have a very high success rate, though. So while it’s possible that the medication won’t work for you, chances are, it will.

Let’s think about side effects now, where the odds of experiencing them might be much higher than 2 out of every 1,000. Let’s say 30 percent of people who take this medication experience vomiting, nausea, and dizziness as a result. Thirty percent is not a majority. It’s still statistically unlikely that you will experience the side effects. Before you take the medication, though, you still want to know what the potential side effects are, but (and this might depend on the severity of the potential side effects), you might not focus too long or hard on your mathematical odds of experiencing them because getting well is your primary concern. Having the medication work is a higher priority than not puking and feeling dizzy. You’ll try the medicine and take the gamble. But you probably don’t think of taking the medicine as a gamble at all. The medicine is supposed to work. It’s statistically very likely to work. But it could still fail you.

A whole lot of research, including many productivity studies (which is what I summarize and assess on this site), use the same principles. Researchers test what happens to a pool of people under certain conditions. When their findings are statistically significant, the researchers say, “This thing happens to these people under these conditions.”

There’s a new trend in medicine called personalized medicine that takes a slightly different approach. Instead of looking at how most people respond to certain treatments, personalized medicine says we should figure out treatments or preventative measures specifically for each person and his or her unique characteristics.

It’s worth repeating that quote from above:

“If it were not for the great variability among individuals,
medicine might as well be a science, not an art.”
–Sir William Osler, 1892 (as cited in Issa, 2007)

The potential for truly personalized medicine in the age of self-quantification and passive data tracking is huge. Imagine if a healthcare team could analyze all the data that you collect every day or could collect every day with the right tools, in addition to sequencing your genes, and make health care recommendations and treatment decisions based on your unique needs. Some of these data might include your diet, your weight, your change in weight as you aged, the amount and type of exercise you get, environmental factors such as air quality or sun exposure, and all the other things that could affect how different medications and preventative treatments would work for you. What if medical scientists could pump some data into a computer to figure out the best ways for you personally to prevent a variety of ailments and treats the ones you get?

Now takes this idea beyond medicine, and you get personalized data.

One very good example of how personalized data is being used comes from the world of competitive sports training. Trainers and coaches spend a lot of time figuring out very specific traits and characteristics about their athletes’ bodies such as heart rate, VO2max, and lactic threshold. They create exercise plans that will maximize the athlete’s performance based on the numbers, and they change the plan as they go depending on how the numbers change. They also tailor the athlete’s diet to sustain the athlete not just from a caloric point of view, but also to get the right amount of muscle build. An American football player will have very different dietary needs than a cyclist or a swimmer.

The Limitations of Personalized Studies

To bring it back to productivity studies for a moment, the question becomes: Why don’t each of us collect data about ourselves and study our own habits to figure out how to maximize our productivity? Why don’t we throw the big data out the window in favor of personalization?

In a perfect world, we would! But data collection is expensive and time-consuming. Finding personalize solutions also requires the ability to compile, process, and analyze data, skills that take time to learn and a great huge heaping pile of objectivity to master. And we’re not very good at being truly objective about ourselves.

What I hear people doing in lieu of true personalized studies is relying on what they think they know about themselves and drawing conclusions that aren’t based on any real data at all. If Dan Ariely’s work (2008) on irrationality has taught me anything, it’s that we lie to ourselves all the time and are very poor analyzers of our own behavior. We might believe we are night owls when in fact we have greater focus in the morning. We might believe we function perfectly well on only six hours of sleep every night, when in fact we’re getting subpar results from ourselves based on what we could be if we were sleeping eight hours a night. And it’s likely we’re actually getting closer to seven hours of sleep, as the amount we sleep is another lie most people tell themselves.

Until it’s time-effective, cost-effective, and possible in other ways to have truly personalized data and recommendations, we’re left looking at the trends from big data, i.e., “research.” Again, remember that studies tells us what most people do, and mathematically speaking, we’re likely to be lumped in with most everyone else.

You’re not a special snowflake, darling, but if you believe you are, you’d better start collecting some hard numbers to prove it.